Update cognitivess_model/modeling_Cognitivess.py

cognitivess_model/modeling_Cognitivess.py

@@ -1,10 +1,5 @@
 # coding=utf-8
-# Copyright 2023 Cognitivess AI and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+# Copyright 2022 Cognitivess and the HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -17,30 +12,31 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""PyTorch Cognitivess model."""
-
 import math
 from typing import List, Optional, Tuple, Union
 
 import torch
+import torch.nn.functional as F
 import torch.utils.checkpoint
 from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
-from ...cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
+from ...cache_utils import Cache, DynamicCache, StaticCache
 from ...modeling_attn_mask_utils import AttentionMaskConverter
+from ...modeling_flash_attention_utils import _flash_attention_forward
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
     CausalLMOutputWithPast,
+    QuestionAnsweringModelOutput,
     SequenceClassifierOutputWithPast,
     TokenClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import ALL_LAYERNORM_LAYERS
 from ...utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
-    is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
     logging,
     replace_return_docstrings,
@@ -48,15 +44,11 @@ from ...utils import (
 from .configuration_Cognitivess import CognitivessConfig
 
 
-if is_flash_attn_2_available():
-    from ...modeling_flash_attention_utils import _flash_attention_forward
-
 logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "CognitivessConfig"
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Cognitivess
 class CognitivessRMSNorm(nn.Module):
     def __init__(self, hidden_size, eps=1e-6):
         """
@@ -74,18 +66,22 @@ class CognitivessRMSNorm(nn.Module):
         return self.weight * hidden_states.to(input_dtype)
 
 
+ALL_LAYERNORM_LAYERS.append(CognitivessRMSNorm)
+
+
 class CognitivessRotaryEmbedding(nn.Module):
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
         super().__init__()
-
+        self.scaling_factor = scaling_factor
         self.dim = dim
         self.max_position_embeddings = max_position_embeddings
         self.base = base
         inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
         self.register_buffer("inv_freq", inv_freq, persistent=False)
+        # For BC we register cos and sin cached
+        self.max_seq_len_cached = max_position_embeddings
 
     @torch.no_grad()
-    # Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.forward
     def forward(self, x, position_ids):
         # x: [bs, num_attention_heads, seq_len, head_size]
         inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
@@ -102,7 +98,35 @@ class CognitivessRotaryEmbedding(nn.Module):
         return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
 
 
-# Copied from transformers.models.llama.modeling_llama.rotate_half
+class CognitivessLinearScalingRotaryEmbedding(CognitivessRotaryEmbedding):
+    """CognitivessRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def forward(self, x, position_ids):
+        # difference to the original RoPE: a scaling factor is aplied to the position ids
+        position_ids = position_ids.float() / self.scaling_factor
+        cos, sin = super().forward(x, position_ids)
+        return cos, sin
+
+
+class CognitivessDynamicNTKScalingRotaryEmbedding(CognitivessRotaryEmbedding):
+    """CognitivessRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def forward(self, x, position_ids):
+        # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (
+                base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim)
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: this may break with compilation
+
+        cos, sin = super().forward(x, position_ids)
+        return cos, sin
+
+
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
     x1 = x[..., : x.shape[-1] // 2]
@@ -110,7 +134,6 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
     """Applies Rotary Position Embedding to the query and key tensors.
 
@@ -141,18 +164,37 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
 class CognitivessMLP(nn.Module):
     def __init__(self, config):
         super().__init__()
+        self.config = config
         self.hidden_size = config.hidden_size
         self.intermediate_size = config.intermediate_size
-        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
         self.act_fn = ACT2FN[config.hidden_act]
 
-    def forward(self, hidden_state):
-        return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))
+    def forward(self, x):
+        if self.config.pretraining_tp > 1:
+            slice = self.intermediate_size // self.config.pretraining_tp
+            gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+            up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+            down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+            gate_proj = torch.cat(
+                [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
+            )
+            up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
+
+            intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+            down_proj = [
+                F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
+            ]
+            down_proj = sum(down_proj)
+        else:
+            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+        return down_proj
 
 
-# Copied from transformers.models.llama.modeling_llama.repeat_kv
 def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
     """
     This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
@@ -166,10 +208,7 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
 
 
 class CognitivessAttention(nn.Module):
-    """
-    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
-    and "Generating Long Sequences with Sparse Transformers".
-    """
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
 
     def __init__(self, config: CognitivessConfig, layer_idx: Optional[int] = None):
         super().__init__()
@@ -185,23 +224,51 @@ class CognitivessAttention(nn.Module):
         self.attention_dropout = config.attention_dropout
         self.hidden_size = config.hidden_size
         self.num_heads = config.num_attention_heads
-        self.head_dim = config.head_dim
+        self.head_dim = self.hidden_size // self.num_heads
         self.num_key_value_heads = config.num_key_value_heads
         self.num_key_value_groups = self.num_heads // self.num_key_value_heads
         self.max_position_embeddings = config.max_position_embeddings
         self.rope_theta = config.rope_theta
         self.is_causal = True
 
-        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
 
-        self.rotary_emb = CognitivessRotaryEmbedding(
-            self.head_dim,
-            max_position_embeddings=self.max_position_embeddings,
-            base=self.rope_theta,
-        )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = CognitivessRotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = CognitivessLinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = CognitivessDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
 
     def forward(
         self,
@@ -212,12 +279,31 @@ class CognitivessAttention(nn.Module):
         output_attentions: bool = False,
         use_cache: bool = False,
         cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         bsz, q_len, _ = hidden_states.size()
 
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
+        if self.config.pretraining_tp > 1:
+            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
+            query_slices = self.q_proj.weight.split(
+                (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
+            )
+            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+            query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
+            query_states = torch.cat(query_states, dim=-1)
+
+            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
+            key_states = torch.cat(key_states, dim=-1)
+
+            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
+            value_states = torch.cat(value_states, dim=-1)
+
+        else:
+            query_states = self.q_proj(hidden_states)
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
 
         query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
         key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
@@ -253,8 +339,14 @@ class CognitivessAttention(nn.Module):
 
         attn_output = attn_output.transpose(1, 2).contiguous()
 
-        attn_output = attn_output.view(bsz, q_len, -1)
-        attn_output = self.o_proj(attn_output)
+        attn_output = attn_output.reshape(bsz, q_len, -1)
+
+        if self.config.pretraining_tp > 1:
+            attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
+            o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
+            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
+        else:
+            attn_output = self.o_proj(attn_output)
 
         if not output_attentions:
             attn_weights = None
@@ -269,7 +361,6 @@ class CognitivessFlashAttention2(CognitivessAttention):
     flash attention and deal with padding tokens in case the input contains any of them.
     """
 
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
@@ -281,13 +372,13 @@ class CognitivessFlashAttention2(CognitivessAttention):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_value: Optional[Cache] = None,
         output_attentions: bool = False,
         use_cache: bool = False,
         cache_position: Optional[torch.LongTensor] = None,
-    ):
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         if isinstance(past_key_value, StaticCache):
             raise ValueError(
                 "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
@@ -302,54 +393,35 @@ class CognitivessFlashAttention2(CognitivessAttention):
         key_states = self.k_proj(hidden_states)
         value_states = self.v_proj(hidden_states)
 
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
         query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
         key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
         value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
 
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            kv_seq_len += cache_position[0]
-
         cos, sin = self.rotary_emb(value_states, position_ids)
         query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
         if past_key_value is not None:
-            # Activate slicing cache only if the config has a value `sliding_windows` attribute
-            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
-            if (
-                getattr(self.config, "sliding_window", None) is not None
-                and kv_seq_len > self.config.sliding_window
-                and cache_has_contents
-            ):
-                slicing_tokens = 1 - self.config.sliding_window
-
-                past_key = past_key_value[self.layer_idx][0]
-                past_value = past_key_value[self.layer_idx][1]
-
-                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
-                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
-
-                if past_key.shape[-2] != self.config.sliding_window - 1:
-                    raise ValueError(
-                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
-                        f" {past_key.shape}"
-                    )
-
-                if attention_mask is not None:
-                    attention_mask = attention_mask[:, slicing_tokens:]
-                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
-
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
             key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
 
-        # repeat k/v heads if n_kv_heads < n_heads
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-        dropout_rate = 0.0 if not self.training else self.attention_dropout
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.attention_dropout if self.training else 0.0
 
         # In PEFT, usually we cast the layer norms in float32 for training stability reasons
         # therefore the input hidden states gets silently casted in float32. Hence, we need
-        # cast them back in float16 just to be sure everything works as expected.
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (CognitivessRMSNorm handles it correctly)
+
         input_dtype = query_states.dtype
         if input_dtype == torch.float32:
             if torch.is_autocast_enabled():
@@ -370,11 +442,6 @@ class CognitivessFlashAttention2(CognitivessAttention):
             key_states = key_states.to(target_dtype)
             value_states = value_states.to(target_dtype)
 
-        # Reashape to the expected shape for Flash Attention
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
         attn_output = _flash_attention_forward(
             query_states,
             key_states,
@@ -382,12 +449,12 @@ class CognitivessFlashAttention2(CognitivessAttention):
             attention_mask,
             q_len,
             dropout=dropout_rate,
-            sliding_window=getattr(self.config, "sliding_window", None),
+            sliding_window=getattr(self, "sliding_window", None),
             use_top_left_mask=self._flash_attn_uses_top_left_mask,
             is_causal=self.is_causal,
         )
 
-        attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_dim).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
         attn_output = self.o_proj(attn_output)
 
         if not output_attentions:
@@ -396,7 +463,6 @@ class CognitivessFlashAttention2(CognitivessAttention):
         return attn_output, attn_weights, past_key_value
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Cognitivess
 class CognitivessSdpaAttention(CognitivessAttention):
     """
     Cognitivess attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
@@ -492,7 +558,6 @@ Cognitivess_ATTENTION_CLASSES = {
 }
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer with Llama->Cognitivess, LLAMA->Cognitivess
 class CognitivessDecoderLayer(nn.Module):
     def __init__(self, config: CognitivessConfig, layer_idx: int):
         super().__init__()
@@ -594,10 +659,11 @@ class CognitivessPreTrainedModel(PreTrainedModel):
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
     _no_split_modules = ["CognitivessDecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
+    _skip_keys_device_placement = ["past_key_values"]
     _supports_flash_attn_2 = True
     _supports_sdpa = True
     _supports_cache_class = True
+    _supports_quantized_cache = True
     _supports_static_cache = True
 
     def _init_weights(self, module):
@@ -633,7 +699,7 @@ Cognitivess_INPUTS_DOCSTRING = r"""
             Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
             [`PreTrainedTokenizer.__call__`] for details.
 
-            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
             `past_key_values`).
 
             If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
@@ -679,6 +745,10 @@ Cognitivess_INPUTS_DOCSTRING = r"""
             more detail.
         return_dict (`bool`, *optional*):
             Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
 """
 
 
@@ -703,10 +773,9 @@ class CognitivessModel(CognitivessPreTrainedModel):
         self.layers = nn.ModuleList(
             [CognitivessDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
         )
-        self._attn_implementation = config._attn_implementation
         self.norm = CognitivessRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
         self.gradient_checkpointing = False
+
         # Initialize weights and apply final processing
         self.post_init()
 
@@ -735,10 +804,8 @@ class CognitivessModel(CognitivessPreTrainedModel):
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         use_cache = use_cache if use_cache is not None else self.config.use_cache
-
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
-        # retrieve input_ids and inputs_embeds
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError(
                 "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
@@ -746,7 +813,7 @@ class CognitivessModel(CognitivessPreTrainedModel):
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
             )
             use_cache = False
 
@@ -754,9 +821,9 @@ class CognitivessModel(CognitivessPreTrainedModel):
             inputs_embeds = self.embed_tokens(input_ids)
 
         return_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+        if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
             return_legacy_cache = True
+            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             logger.warning_once(
                 "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
                 "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)"
@@ -767,14 +834,14 @@ class CognitivessModel(CognitivessPreTrainedModel):
             cache_position = torch.arange(
                 past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
             )
-
         if position_ids is None:
             position_ids = cache_position.unsqueeze(0)
 
         causal_mask = self._update_causal_mask(
-            attention_mask, inputs_embeds, cache_position, past_key_values, use_cache, output_attentions
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
         )
 
+        # embed positions
         hidden_states = inputs_embeds
 
         # decoder layers
@@ -841,7 +908,6 @@ class CognitivessModel(CognitivessPreTrainedModel):
         input_tensor: torch.Tensor,
         cache_position: torch.Tensor,
         past_key_values: Cache,
-        use_cache: bool,
         output_attentions: bool,
     ):
         # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
@@ -849,15 +915,7 @@ class CognitivessModel(CognitivessPreTrainedModel):
         # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
         # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
 
-        if self._attn_implementation == "flash_attention_2":
-            if attention_mask is not None and use_cache:
-                is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
-                if is_padding_right:
-                    raise ValueError(
-                        "You are attempting to perform batched generation with padding_side='right'"
-                        " this may lead to unexpected behaviour for Flash Attention version of Cognitivess. Make sure to "
-                        " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
-                    )
+        if self.config._attn_implementation == "flash_attention_2":
             if attention_mask is not None and 0.0 in attention_mask:
                 return attention_mask
             return None
@@ -865,22 +923,15 @@ class CognitivessModel(CognitivessPreTrainedModel):
         # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
         # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
         # to infer the attention mask.
-
-        # cache_position must be valid here no matter which cache we use
-        past_seen_tokens = cache_position[0] if past_key_values is not None else 0
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
         using_static_cache = isinstance(past_key_values, StaticCache)
-        using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
 
-        if (
-            self.config._attn_implementation == "sdpa"
-            and not (using_static_cache or using_sliding_window_cache)
-            and not output_attentions
-        ):
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
             if AttentionMaskConverter._ignore_causal_mask_sdpa(
                 attention_mask,
                 inputs_embeds=input_tensor,
                 past_key_values_length=past_seen_tokens,
-                sliding_window=self.config.sliding_window,
                 is_training=self.training,
             ):
                 return None
@@ -888,13 +939,8 @@ class CognitivessModel(CognitivessPreTrainedModel):
         dtype, device = input_tensor.dtype, input_tensor.device
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
-        # SlidingWindowCache
-        if using_sliding_window_cache:
-            target_length = max(sequence_length, self.config.sliding_window)
-        # StaticCache
-        elif using_static_cache:
+        if using_static_cache:
             target_length = past_key_values.get_max_length()
-        # DynamicCache or no cache
         else:
             target_length = (
                 attention_mask.shape[-1]
@@ -911,25 +957,18 @@ class CognitivessModel(CognitivessPreTrainedModel):
             causal_mask = torch.full(
                 (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
             )
-            exclude_mask = torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
-            if self.config.sliding_window is not None:
-                if not using_sliding_window_cache or sequence_length > self.config.sliding_window:
-                    exclude_mask.bitwise_or_(
-                        torch.arange(target_length, device=device)
-                        <= (cache_position.reshape(-1, 1) - self.config.sliding_window)
-                    )
-            causal_mask *= exclude_mask
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
             causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
             if attention_mask is not None:
                 causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-                if attention_mask.dim() == 2:
-                    mask_length = attention_mask.shape[-1]
-                    padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
-                    padding_mask = padding_mask == 0
-                    causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-                        padding_mask, min_dtype
-                    )
-
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
         if (
             self.config._attn_implementation == "sdpa"
             and attention_mask is not None
@@ -1015,7 +1054,6 @@ class CognitivessForCausalLM(CognitivessPreTrainedModel):
         >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
         "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
         ```"""
-
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -1037,7 +1075,12 @@ class CognitivessForCausalLM(CognitivessPreTrainedModel):
         )
 
         hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
+        if self.config.pretraining_tp > 1:
+            lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+            logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+            logits = torch.cat(logits, dim=-1)
+        else:
+            logits = self.lm_head(hidden_states)
         logits = logits.float()
 
         loss = None
@@ -1046,11 +1089,11 @@ class CognitivessForCausalLM(CognitivessPreTrainedModel):
             shift_logits = logits[..., :-1, :].contiguous()
             shift_labels = labels[..., 1:].contiguous()
             # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
             shift_logits = shift_logits.view(-1, self.config.vocab_size)
             shift_labels = shift_labels.view(-1)
-            # Ensure tensors are on the same device
+            # Enable model parallelism
             shift_labels = shift_labels.to(shift_logits.device)
-            loss_fct = CrossEntropyLoss()
             loss = loss_fct(shift_logits, shift_labels)
 
         if not return_dict:
@@ -1065,7 +1108,6 @@ class CognitivessForCausalLM(CognitivessPreTrainedModel):
             attentions=outputs.attentions,
         )
 
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.prepare_inputs_for_generation
     def prepare_inputs_for_generation(
         self,
         input_ids,
@@ -1126,7 +1168,6 @@ class CognitivessForCausalLM(CognitivessPreTrainedModel):
     """,
     Cognitivess_START_DOCSTRING,
 )
-# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Cognitivess, LLAMA->Cognitivess
 class CognitivessForSequenceClassification(CognitivessPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
@@ -1235,6 +1276,105 @@ class CognitivessForSequenceClassification(CognitivessPreTrainedModel):
         )
 
 
+@add_start_docstrings(
+    """
+The Cognitivess Model transformer with a span classification head on top for extractive question-answering tasks like
+SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    Cognitivess_START_DOCSTRING,
+)
+class CognitivessForQuestionAnswering(CognitivessPreTrainedModel):
+    base_model_prefix = "transformer"
+
+    # Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Cognitivess
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = CognitivessModel(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.transformer.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.transformer.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(Cognitivess_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1).to(start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
 @add_start_docstrings(
     """
     The Cognitivess Model transformer with a token classification head on top (a linear layer on top of the hidden-states
@@ -1242,7 +1382,6 @@ class CognitivessForSequenceClassification(CognitivessPreTrainedModel):
     """,
     Cognitivess_START_DOCSTRING,
 )
-# Copied from transformers.models.llama.modeling_llama.LlamaForTokenClassification with Llama->Cognitivess, LLAMA->Cognitivess
 class CognitivessForTokenClassification(CognitivessPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)